RFE
#include <Skigen/FeatureSelection>
template <typename Estimator>
class Skigen::RFE(estimator, n_features_to_select=std::nullopt, step=1, verbose=0)
Feature ranking with recursive feature elimination.
Mirrors sklearn.feature_selection.RFE.
Given an external estimator that assigns weights to features, recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained. Then, the least important features are pruned from current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached.
Attributes:
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estimator : Estimator
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n_features : int
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support : BoolMaskType
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ranking : const Eigen::VectorXi
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is_fitted : bool
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n_features_in : IndexType
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get_support_indices : Eigen::VectorXi